Ritchie Bros. Heavy Equipment Auction Results

Ritchie Bros. Auctioneers - auction results for heavy equipment is the transaction-side record of the world's largest heavy equipment auctioneer, operating under the RB Global umbrella since its 2023 combination with IAA. One corporate family runs several marketplaces - Ritchie Bros.' flagship unreserved auctions, IronPlanet, GovPlanet, TruckPlanet and Marketplace-E - each publishing item-level lots for construction machinery, mining equipment and trucks. At verification in August 2026 the IronPlanet construction category alone enumerated 39,911 items, government surplus added 6,729 more, and a single dated sale could carry 1,611 lots.

Every record is one machine at one moment in one sale: lot number, make, model, model year, meter hours, sale event, sale date and yard location, with the hammer price attached wherever sale results publish. That is the piece listing marketplaces cannot give you. An asking price is an opinion held by a seller until a buyer disagrees; an unreserved auction hammer is an outcome. Get a sample of this dataset and the rows arrive typed and joined-ready rather than copied off a bidder-facing site.

Four illustrative rows in the delivered column order - two lots out of one mining sale, one out of a regional construction sale, and one lot whose result has yet to publish:

# one row per lot, per sale event - illustrative rows in the delivered column order
lot number : 2147
make       : Caterpillar   model: 336 GC        year: 2019
meter hours: 4210          sale event: wa-eofy-mining-auction
sale date  : 2026-09-02    hammer: 132000       location: Seattle, WA

lot number : 0642
make       : Komatsu       model: PC210LC-11    year: 2021
meter hours: 1875          sale event: us-sacramento-construction-auction
sale date  : 2026-08-19    hammer: 118500       location: Sacramento, CA

lot number : 0913
make       : John Deere    model: 210G          year: 2020
meter hours: 2689          sale event: wa-eofy-mining-auction
sale date  : 2026-09-02    hammer: 96500        location: Seattle, WA

lot number : 1205
make       : Volvo         model: L110H         year: 2018
meter hours: 5340          sale event: specialist-tunnel-excavation-sale
sale date  : 2026-07-15    hammer: <where results publish>

Read the anatomy rather than the machines. Hours against hammer is depreciation made visible: the 4,210-hour 2019 excavator and the 1,875-hour 2021 one bracket what age and wear actually subtract in this market. Two lots sharing a sale event show how cohorts form - same auction, same bidder pool, same day - which is what makes clean comp sets. And the fourth row is deliberately unfinished: until a sale's results publish, the price column stays honestly empty instead of wearing a stale number.

Nine documented fields define the lot record: five describe the machine (make, model, year, meter hours, location), three describe its sale (sale event, sale date, lot number) and one records the outcome (hammer/sold price). Definitions were reconstructed against the auction layout during the research pass rather than lifted from a publisher data dictionary, and they are confirmed against real rows at first delivery. Where a field's observed values depend on sale-results publication - hammer price chiefly - they fold under additional fields on request rather than pretending at universality.

Geography - global across the marketplace family: North America, Europe, the Middle East, Asia-Pacific and Latin America. Regional yards mean the same machine class clears in several markets, which is precisely what cross-region benchmarking needs.

Temporal - a rolling calendar of dated sale events going forward, with realized results retained for sales already held. Because each event is dated, successive sales accumulate naturally into the panel that residual-value curves are fitted on.

Granularity - one record per equipment lot per sale event. No category averages, no monthly blends: individual machines with individual hours, individually hammered.

Set against the wider catalog - an average quality score of 7.81 across all 1,744 datasets - this slice scores 7/10: strong on scale, structure and the rarity of transaction prices, marked down honestly for inferred field definitions and uneven price publication. The best construction-machinery datasets ranking shows where it sits on the shelf.

API, files, or your warehouse. Daily, weekly, or hourly.

Every delivery ships the complete field dictionary above, the sample rows and the coverage profile mapped to your categories, regions and sale types - no integration archaeology on your side.

• Residual-value and depreciation modeling - meter hours against hammer price across same-model lots builds empirical depreciation surfaces on transactions, the input lenders and lessors pretend their residual tables have.
• Capex-cycle reads - realized prices and lot volumes by region track fleet investment appetite between official statistics, and occasionally ahead of it.
• Competitor disposal watching - dated sale calendars reveal who is divesting what months before filings or press releases admit it.
• Consignor prospecting - repeat sellers visible across sale events are dealers and fleet owners in active divestment, the best-timed prospects a sales team can ask for.
• Cross-region benchmarking - identical categories clearing at different yards quantify where used iron is abundant and where it is thin.

Data scientists and ML engineers get a tidy nine-column transaction table with coverage flags already attached - training data that knows what it doesn't cover. Investors and quants read realized prices as a coincident indicator of equipment capex. Competitive intelligence teams watch rivals' disposal activity through dated calendars. Market researchers and consultants benchmark used-equipment values across regions with transaction numbers instead of surveyed guesses. Journalists and academics cite dated sale events and lot outcomes when documenting market turning points. Persona workflows live at market researchers x construction machinery and competitive intel product teams x construction machinery.

Within this industry the pricing sources split cleanly by side of trade. MachineryTrader construction equipment listings and TruckPaper commercial trucks and trailers listings carry dealer asking prices across US inventory - deep, current and negotiable by design. The Kaggle heavy equipment pricing snapshot freezes 1,742 US listings at one moment - handy for prototyping, useless for tracking a moving market. This dataset supplies the third leg neither offers: realized transaction outcomes from unreserved auctions, worldwide. Asking prices tell you what sellers hope; hammer prices tell you what buyers actually paid, and serious valuation work eventually wants both.

Four things worth knowing upfront. First, field definitions are reconstructed rather than publisher-documented, so they are confirmed against real rows at first delivery - flag anything that looks off and it gets fixed before volume flows. Second, hammer-price publication varies by marketplace and by sale, so price-dependent analyses should be scoped to the sales that publish; the coverage flag rides with every row. Third, meter hours are the recorded hour-meter readings at consignment - reported figures, not audits - which is true of every auction-derived hours field anywhere in this market. Fourth, calendar depth differs by region and marketplace, so name your regions and equipment classes in the sample request and the sizing comes back honest.

Because the raw artifact is a shopper-facing marketplace built for bidders, and valuation work wants typed rows. Datadory delivers the lot records as clean columns with definitions and examples attached, normalizes make and model strings toward canonical identifiers so joins hold, attaches category-taxonomy tags beside raw descriptions, and keeps successive sale events accumulating on a schedule so price movement becomes observable history rather than isolated prints. Start from the construction machinery data hub, the definition of heavy equipment auction results, or the full catalog - then get a sample of this dataset.